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Names of BS numpy arrays are changed : BS_data and BS_data_section become, respectively, BS_cross_section and BS_stream_bed. In general, name of variables including data under river bottom line is composed with _cross_section. Name of variables with NaN under river bottom line is composed with _stream_bed.
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@@ -10,7 +10,8 @@ from matplotlib.colors import LogNorm
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# path_noise_data = "/home/bmoudjed/Documents/3 SSC acoustic meas project/Graphical interface project/" \
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# "Data/AcousticNoise_data/20180107121600.aqa"
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class AcousticDataLoader():
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class AcousticDataLoader:
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def __init__(self, path_BS_raw_data: str):
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@@ -48,6 +49,9 @@ class AcousticDataLoader():
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self._gain_rx = self._data_BS.RxGain
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self._gain_tx = self._data_BS.TxGain
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print(self._r[0, :][1] - self._r[1, :][0])
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print(self._kt)
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# self._snr = np.array([])
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# self._snr_reshape = np.array([])
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# self._time_snr = np.array([])
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@@ -61,11 +65,16 @@ class AcousticDataLoader():
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# print(np.where((self._time) == 155)[0][0])
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# fig, ax = plt.subplots(nrows=1, ncols=1)
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# ax.pcolormesh(self._time, self._r, (self._BS_raw_data[:, 1, :]),
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# ax.pcolormesh(self._time[0, :2200], -self._r[0, :], (self._BS_raw_data[0, :, :2200]),
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# cmap='viridis',
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# norm=LogNorm(vmin=1e-5, vmax=np.max(self._BS_raw_data[0, :, :2200]))) # , shading='gouraud')
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# ax.pcolormesh(range(self._BS_raw_data.shape[2]), range(self._BS_raw_data.shape[0]), self._BS_raw_data[:, 1, :], cmap='viridis',
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# norm=LogNorm(vmin=1e-5, vmax=np.max(self._BS_raw_data[:, 0, :]))) # , shading='gouraud')
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# # ax.pcolormesh(range(self._BS_raw_data.shape[2]), range(self._BS_raw_data.shape[0]), self._BS_raw_data[:, 1, :], cmap='viridis',
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# # norm=LogNorm(vmin=1e-5, vmax=np.max(self._BS_raw_data[:, 0, :]))) # , shading='gouraud')
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# plt.show()
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# --- Plot vertical profile for bottom detection ---
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# fig2, ax2 = plt.subplots(nrows=1, ncols=1, layout="constrained")
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# ax2.plot(self._BS_raw_data[0, :, 1], -self._r[0], "k.-")
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# plt.show()
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# fig, ax = plt.subplots(nrows=1, ncols=1)
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@@ -77,6 +86,7 @@ class AcousticDataLoader():
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# self.reshape_BS_raw_cross_section()
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# self.reshape_r()
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# self.reshape_t()
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# self.compute_r_2D()
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def reshape_BS_raw_cross_section(self):
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BS_raw_cross_section = np.reshape(self._BS_raw_data,
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@@ -96,7 +106,10 @@ class AcousticDataLoader():
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return r
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def compute_r_2D(self):
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r2D = np.repeat(self._r, self._time.size, axis=1)
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r2D = np.zeros((self._freq.shape[0], self._r.shape[1], self._time.shape[1]))
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for f, _ in enumerate(self._freq):
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r2D[f, :, :] = np.repeat(np.transpose(self._r[0, :])[:, np.newaxis], self._time.shape[1], axis=1)
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print(r2D.shape)
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return r2D
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def reshape_t(self):
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@@ -1,10 +1,12 @@
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from Model.AquascatDataLoader import RawAquascatData
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import numpy as np
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import pandas as pd
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import datetime
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import matplotlib.pyplot as plt
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from matplotlib.colors import LogNorm, BoundaryNorm
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from copy import deepcopy
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from scipy.signal import savgol_filter
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from Model.udt_extract.raw_extract import raw_extract
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# raw_20210519_102332.udt raw_20210520_135452.udt raw_20210525_092759.udt raw_20210525_080454.udt
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@@ -31,7 +33,7 @@ from Model.udt_extract.raw_extract import raw_extract
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# "raw_20210526_153310.udt"
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class AcousticDataLoaderUBSediFlow():
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class AcousticDataLoaderUBSediFlow:
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def __init__(self, path_BS_raw_data: str):
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@@ -45,6 +47,10 @@ class AcousticDataLoaderUBSediFlow():
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device_name, time_begin, time_end, param_us_dicts, data_us_dicts, data_dicts, settings_dict \
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= raw_extract(self.path_BS_raw_data)
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print(f"device_name : {device_name}")
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print(f"time_begin : {time_begin}")
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print(f"settings_dict : {settings_dict}")
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# --- Date and Hour of measurements read on udt data file ---
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filename = self.path_BS_raw_data[-23:]
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date_and_time = datetime.datetime(year=int(filename[4:8]),
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@@ -63,28 +69,47 @@ class AcousticDataLoaderUBSediFlow():
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self._time = np.array([[]])
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self._time_snr = np.array([[]])
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self._BS_raw_data = np.array([[[]]])
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self._SNR_data = np.array([[[]]])
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# self._SNR_data = np.array([[[]]])
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time_len = []
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time_snr_len = []
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for config in param_us_dicts.keys():
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print("-x-x-x-x-x-x-x-x-x-x-x-x-x-x-x-x-x-x-x-x-x-x")
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print(f"config : {config} \n")
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# print("-x-x-x-x-x-x-x-x-x-x-x-x-x-x-x-x-x-x-x-x-x-x")
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# print(f"config : {config} \n")
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for channel in param_us_dicts[config].keys():
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print("-x-x-x-x-x-x-x-x-x-x-x-x-x-x-x-x-x-x-x-x-x-x")
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print(f"channel : {channel} \n")
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# print(param_us_dicts[1][1])
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# print(f"channel : {channel} \n")
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print(param_us_dicts[config][channel])
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# print(data_us_dicts[config][channel]['echo_avg_profile'])
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# --- Frequencies ---
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self._freq = np.append(self._freq, param_us_dicts[config][channel]['f0'])
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# --- Depth for each frequencies ---
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depth = [param_us_dicts[config][channel]['r_cell1'] * i
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print("r_dcell : ", param_us_dicts[config][channel]['r_dcell'])
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print("n_cell : ", param_us_dicts[config][channel]['n_cell'])
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depth = [param_us_dicts[config][channel]['r_dcell'] * i
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for i in list(range(param_us_dicts[config][channel]['n_cell']))]
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print(f"depth : {depth}")
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print(f"lenght of depth : {len(depth)}")
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if self._r.shape[1] == 0:
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self._r = np.array([depth])
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else:
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self._r = np.append(self._r, [depth], axis=0)
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if len(depth) == self._r.shape[1]:
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print("Je suis là")
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print(f"depth lenght : {len(depth)}")
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print(f"r shape : {self._r.shape}")
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self._r = np.append(self._r, np.array([depth]), axis=0)
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print("C'est encore moi")
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elif len(depth) < self._r.shape[1]:
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print(f"depth lenght : {len(depth)}")
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self._r = self._r[:, :len(depth)]
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self._r = np.append(self._r, np.array([depth]), axis=0)
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print(f"r shape : {self._r.shape}")
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elif len(depth) > self._r.shape[1]:
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print(f"depth lenght : {len(depth)}")
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self._r = np.append(self._r, np.array([depth[:self._r.shape[1]]]), axis=0)
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print(f"r shape : {self._r.shape}")
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print(f"self._r : {self._r.shape}")
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# --- BS Time for each frequencies ---
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time = [[(t - data_us_dicts[config][channel]['echo_avg_profile']['time'][0]).total_seconds()
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@@ -92,72 +117,72 @@ class AcousticDataLoaderUBSediFlow():
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time_len = np.append(time_len, len(time[0]))
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if len(time_len) == 1:
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# print(f"1 time length : {len(time[0])}")
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print(f"1 time length : {len(time[0])}")
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self._time = np.array(time)
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# print(f"self._time.shape {self._time.shape}")
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print(f"self._time.shape {self._time.shape}")
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elif self._time.shape[1] == len(time[0]):
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# print(f"2 time length : {len(time[0])}")
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print(f"2 time length : {len(time[0])}")
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self._time = np.append(self._time, time, axis=0)
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# print(f"self._time.shape {self._time.shape}")
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print(f"self._time.shape {self._time.shape}")
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elif self._time.shape[1] > len(time[0]):
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# print(f"3 time length : {len(time[0])}")
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print(f"3 time length : {len(time[0])}")
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# print(f"self._time.shape {self._time.shape}")
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# print([int(np.min(time_len)) + int(i) - 1 for i in range(1, int(np.max(time_len))-int(np.min(time_len))+1)])
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self._time = np.delete(self._time,
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[int(np.min(time_len)) + int(i) - 1 for i in range(1, int(np.max(time_len))-int(np.min(time_len))+1)],
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axis=1)
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self._time = np.append(self._time, time, axis=0)
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# print(f"self._time.shape {self._time.shape}")
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print(f"self._time.shape {self._time.shape}")
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elif self._time.shape[1] < len(time[0]):
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# print(f"4 time length : {len(time[0])}")
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print(f"4 time length : {len(time[0])}")
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time = time[:int(np.max(time_len)) - (int(np.max(time_len)) - int(np.min(time_len)))]
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self._time = np.append(self._time, time, axis=0)
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# print(f"self._time.shape {self._time.shape}")
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print(f"self._time.shape {self._time.shape}")
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# --- SNR Time for each frequencies ---
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time_snr = [[(t - data_us_dicts[config][channel]['snr_doppler_avg_profile']['time'][0]).total_seconds()
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for t in data_us_dicts[config][channel]['snr_doppler_avg_profile']['time']]]
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time_snr_len = np.append(time_snr_len, len(time_snr[0]))
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if len(time_snr_len) == 1:
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# print(f"1 time length : {len(time[0])}")
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self._time_snr = np.array(time_snr)
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# print(f"self._time.shape {self._time.shape}")
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elif self._time_snr.shape[1] == len(time_snr[0]):
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# print(f"2 time length : {len(time[0])}")
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self._time_snr = np.append(self._time_snr, time_snr, axis=0)
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# print(f"self._time.shape {self._time.shape}")
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elif self._time_snr.shape[1] > len(time_snr[0]):
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# print(f"3 time length : {len(time[0])}")
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# print(f"self._time.shape {self._time.shape}")
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# print([int(np.min(time_len)) + int(i) - 1 for i in range(1, int(np.max(time_len))-int(np.min(time_len))+1)])
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self._time_snr = np.delete(self._time_snr,
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[int(np.min(time_snr_len)) + int(i) - 1 for i in
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range(1, int(np.max(time_snr_len)) - int(np.min(time_snr_len)) + 1)],
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axis=1)
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self._time_snr = np.append(self._time_snr, time_snr, axis=0)
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# print(f"self._time.shape {self._time.shape}")
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elif self._time_snr.shape[1] < len(time_snr[0]):
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# print(f"4 time length : {len(time[0])}")
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time_snr = time_snr[:int(np.max(time_snr_len)) - (int(np.max(time_snr_len)) - int(np.min(time_snr_len)))]
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self._time_snr = np.append(self._time_snr, time_snr, axis=0)
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# print(f"self._time.shape {self._time.shape}")
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# time_snr = [[(t - data_us_dicts[config][channel]['snr_doppler_avg_profile']['time'][0]).total_seconds()
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# for t in data_us_dicts[config][channel]['snr_doppler_avg_profile']['time']]]
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# time_snr_len = np.append(time_snr_len, len(time_snr[0]))
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#
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# if len(time_snr_len) == 1:
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# # print(f"1 time length : {len(time[0])}")
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# self._time_snr = np.array(time_snr)
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# # print(f"self._time.shape {self._time.shape}")
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# elif self._time_snr.shape[1] == len(time_snr[0]):
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# # print(f"2 time length : {len(time[0])}")
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# self._time_snr = np.append(self._time_snr, time_snr, axis=0)
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# # print(f"self._time.shape {self._time.shape}")
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# elif self._time_snr.shape[1] > len(time_snr[0]):
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# # print(f"3 time length : {len(time[0])}")
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# # print(f"self._time.shape {self._time.shape}")
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# # print([int(np.min(time_len)) + int(i) - 1 for i in range(1, int(np.max(time_len))-int(np.min(time_len))+1)])
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# self._time_snr = np.delete(self._time_snr,
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# [int(np.min(time_snr_len)) + int(i) - 1 for i in
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# range(1, int(np.max(time_snr_len)) - int(np.min(time_snr_len)) + 1)],
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# axis=1)
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# self._time_snr = np.append(self._time_snr, time_snr, axis=0)
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# # print(f"self._time.shape {self._time.shape}")
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# elif self._time_snr.shape[1] < len(time_snr[0]):
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# # print(f"4 time length : {len(time[0])}")
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# time_snr = time_snr[:int(np.max(time_snr_len)) - (int(np.max(time_snr_len)) - int(np.min(time_snr_len)))]
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# self._time_snr = np.append(self._time_snr, time_snr, axis=0)
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# # print(f"self._time.shape {self._time.shape}")
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# --- US Backscatter raw signal + SNR data ---
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BS_data = np.array([[]])
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if config == 1:
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BS_data = np.array([data_us_dicts[config][channel]['echo_avg_profile']['data'][0]])
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print("BS_data shape ", BS_data.shape)
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# print("BS_data shape ", BS_data.shape)
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for i in range(self._time.shape[1]):
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BS_data = np.append(BS_data,
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np.array([data_us_dicts[config][channel]['echo_avg_profile']['data'][i]]),
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axis=0)
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print("BS_data shape ", BS_data.shape)
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print("0. BS_data shape ", BS_data.shape)
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self._BS_raw_data = np.array([BS_data[:self._time.shape[1], :].transpose()])
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print("BS_raw_data shape ", self._BS_raw_data.shape)
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print("0. BS_raw_data shape ", self._BS_raw_data.shape)
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# fig, ax = plt.subplots(nrows=1, ncols=1, layout="constrained")
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# pcm = ax.pcolormesh(list(range(self._BS_raw_data.shape[2])), list(range(self._BS_raw_data.shape[1])),
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@@ -169,28 +194,40 @@ class AcousticDataLoaderUBSediFlow():
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else:
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BS_data = np.array([data_us_dicts[config][channel]['echo_avg_profile']['data'][0]])
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print("BS_data shape ", BS_data.shape)
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# print("BS_data shape ", BS_data.shape)
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for i in range(self._time.shape[1]):
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BS_data = np.append(BS_data,
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np.array(
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[data_us_dicts[config][channel]['echo_avg_profile']['data'][i]]),
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axis=0)
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print("BS_data shape ", BS_data.shape)
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print("1. BS_data shape ", BS_data.shape)
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if BS_data.shape[0] > self._BS_raw_data.shape[2]:
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#-----------------------------------------------------------------------------------------------------------------------
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# Ici il faut écrire les conditions sur les tailles selon r et selon time
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# donc sur BS_data.shape[0] (time) et BS_data.shape[1] (depth)
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#-----------------------------------------------------------------------------------------------------------------------
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# 1- time shape > BS data shape
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# <=> data recorded with the frequency are longer than data recorded with the other lower frequencies
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if (BS_data.shape[0] > self._BS_raw_data.shape[2]):
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self._BS_raw_data = np.append(self._BS_raw_data,
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np.array([BS_data[:self._BS_raw_data.shape[2], :].transpose()]),
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axis=0)
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# 2- time shape < BS data shape
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# <=> data recorded with the frequency are shorter than data recorded with the other lower frequencies
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elif BS_data.shape[0] < self._BS_raw_data.shape[2]:
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self._BS_raw_data = np.append(self._BS_raw_data[config-1, :, BS_data.shape[0]],
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np.array([BS_data.transpose()]),
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axis=0)
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# 3- time shape = BS data shape
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# <=> data recorded with the frequency have the same duration than data recorded with the other lower frequency
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else:
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self._BS_raw_data = np.append(self._BS_raw_data, np.array([BS_data.transpose()]), axis=0)
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print("BS_raw_data shape ", self._BS_raw_data.shape)
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print("1. BS_raw_data shape ", self._BS_raw_data.shape)
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# if f == 0:
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# print(np.array(data_us_dicts[config][channel]['echo_avg_profile']['data'][0]).shape)
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@@ -298,11 +335,11 @@ class AcousticDataLoaderUBSediFlow():
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# --- Plot Backscatter US data ---
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# fig, ax = plt.subplots(nrows=1, ncols=1, layout="constrained")
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# # pcm = ax.pcolormesh(self._time[0, :], self._r[0, :], np.log(self._BS_raw_data[0, :, :]),
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# # cmap='Blues')#, shading='gouraud')
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# pcm = ax.pcolormesh(list(range(self._BS_raw_data.shape[2])), list(range(self._BS_raw_data.shape[1])),
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# np.log(self._BS_raw_data[0, :, :]),
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# cmap='Blues') # , shading='gouraud')
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# pcm = ax.pcolormesh(self._time[0, :], -self._r[0, :], np.log(self._BS_raw_data[0, :, :]),
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# cmap='Blues')#, shading='gouraud')
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# # pcm = ax.pcolormesh(list(range(self._BS_raw_data.shape[2])), list(range(self._BS_raw_data.shape[1])),
|
||||
# # np.log(self._BS_raw_data[0, :, :]),
|
||||
# # cmap='Blues') # , shading='gouraud')
|
||||
# # norm=LogNorm(vmin=np.min(self._BS_raw_data[f, :, :]), vmax=np.max(self._BS_raw_data[f, :, :])), shading='gouraud')
|
||||
# # ax.pcolormesh(range(self._BS_raw_data.shape[2]), range(self._BS_raw_data.shape[0]), self._BS_raw_data[:, 1, :], cmap='viridis',
|
||||
# # norm=LogNorm(vmin=1e-5, vmax=np.max(self._BS_raw_data[:, 0, :]))) # , shading='gouraud')
|
||||
@@ -317,12 +354,38 @@ class AcousticDataLoaderUBSediFlow():
|
||||
# # pcm = ax[f].pcolormesh(list(range(self._BS_raw_data.shape[2])), list(range(self._BS_raw_data.shape[1])),
|
||||
# # np.log(self._BS_raw_data[f, :, :]),
|
||||
# # cmap='Blues', shading='gouraud')
|
||||
# pcm = ax[f].pcolormesh(self._time[f, :], self._r[f, :], np.log(self._BS_raw_data[f, :, :]),
|
||||
# cmap='viridis', shading='gouraud')
|
||||
# pcm = ax[f].pcolormesh(self._time[f, 50:247], -self._r[f, :], np.log(self._BS_raw_data[f, :, 50:247]),
|
||||
# cmap='Blues')#, shading='gouraud')
|
||||
# # norm=LogNorm(vmin=np.min(self._BS_raw_data[f, :, :]), vmax=np.max(self._BS_raw_data[f, :, :])), shading='gouraud')
|
||||
# # ax.pcolormesh(range(self._BS_raw_data.shape[2]), range(self._BS_raw_data.shape[0]), self._BS_raw_data[:, 1, :], cmap='viridis',
|
||||
# # norm=LogNorm(vmin=1e-5, vmax=np.max(self._BS_raw_data[:, 0, :]))) # , shading='gouraud')
|
||||
# fig.colorbar(pcm, ax=ax[:], shrink=1, location='right')
|
||||
# # plt.show()
|
||||
#
|
||||
# # --- Smooth value with savgol_filter ---
|
||||
# BS_smooth = deepcopy(self._BS_raw_data[0, :, :])
|
||||
# for k in range(self._time[0, :].shape[0]):
|
||||
# BS_smooth[:, k] = savgol_filter(BS_smooth[:, k], 10, 2)
|
||||
#
|
||||
# fig1, ax1 = plt.subplots(nrows=1, ncols=1, layout="constrained")
|
||||
# pcm1 = ax1.pcolormesh(self._time[0, :], -self._r[0, :], np.log(BS_smooth[:, :]), cmap='Blues')
|
||||
# fig1.colorbar(pcm1, ax=ax1, shrink=1, location='right')
|
||||
#
|
||||
# # --- Plot vertical profile for bottom detection ---
|
||||
# n = 60
|
||||
# t0 = 200
|
||||
# t1 = np.where(np.abs(self._time[0, :] - t0) == np.nanmin(np.abs(self._time[0, :] - t0)))[0][0]
|
||||
# # print(np.abs(self._time[0, :] - 200))
|
||||
# # print(f"x0 = {x0}")
|
||||
# r1 = 98
|
||||
# r2 = 150
|
||||
# fig2, ax2 = plt.subplots(nrows=1, ncols=n, layout="constrained")
|
||||
# for i in range(n):
|
||||
# ax2[i].plot(self._BS_raw_data[0, r1:r2, t1+i], -self._r[0, r1:r2], 'b')
|
||||
# ax2[i].plot(BS_smooth[r1:r2, t1+i], -self._r[0, r1:r2], 'r')
|
||||
# ax2[i].set_xticks([])
|
||||
# if i != 0:
|
||||
# ax2[i].set_yticks([])
|
||||
# plt.show()
|
||||
|
||||
# --- Plot SNR data ---
|
||||
@@ -373,6 +436,7 @@ class AcousticDataLoaderUBSediFlow():
|
||||
# self.reshape_BS_raw_cross_section()
|
||||
# self.reshape_r()
|
||||
# self.reshape_t()
|
||||
# self.compute_r_2D()
|
||||
|
||||
# Lecture du fichier excel
|
||||
|
||||
@@ -425,6 +489,13 @@ class AcousticDataLoaderUBSediFlow():
|
||||
# print(r.shape)
|
||||
return r
|
||||
|
||||
def compute_r_2D(self):
|
||||
r2D = np.zeros((self._freq.shape[0], self._r.shape[1], self._time.shape[1]))
|
||||
for f, _ in enumerate(self._freq):
|
||||
r2D[f, :, :] = np.repeat(np.transpose(self._r[0, :])[:, np.newaxis], self._time.shape[1], axis=1)
|
||||
print("r2D.shape ", r2D.shape)
|
||||
return r2D
|
||||
|
||||
# def compute_r_2D(self):
|
||||
# r2D = np.repeat(self._r, self._time.size, axis=1)
|
||||
# return r2D
|
||||
@@ -443,6 +514,12 @@ class AcousticDataLoaderUBSediFlow():
|
||||
# print(t.shape)
|
||||
return t
|
||||
|
||||
def detect_bottom(self):
|
||||
rmin = 2.5
|
||||
rmax = 3.5
|
||||
|
||||
|
||||
|
||||
# def concatenate_data(self):
|
||||
|
||||
# self.reshape_BS_raw_cross_section()
|
||||
|
||||
Reference in new issue
Block a user